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Data"],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., LASSO) methods have hyperparameters (e.g., top-\n            <jats:italic>k<\/jats:italic>\n            , score thresholding) and tie to specific models, thus, hard to generalize; wrapper methods search a feature subset in a huge discrete space and is computationally costly. To transform the way of feature selection, we regard a selected feature subset as a selection decision token sequence and reformulate feature selection as a deep sequential generative learning task that distills feature knowledge and generates decision sequences. Our method includes three steps: (1) We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores. (2) We leverage the trained feature subset utility evaluator as a gradient provider to guide the identification of the optimal feature subset embedding; (3) We decode the optimal feature subset embedding to autoregressively generate the best feature selection decision sequence with autostop. Extensive experimental results show this generative perspective is effective and generic, without large discrete search space and expert-specific hyperparameters. The code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"http:\/\/tinyurl.com\/FSDSGL\">http:\/\/tinyurl.com\/FSDSGL<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3687485","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T16:19:46Z","timestamp":1723220386000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Feature Selection as Deep Sequential Generative Learning"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6196-0287","authenticated-orcid":false,"given":"Wangyang","family":"Ying","sequence":"first","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3948-0059","authenticated-orcid":false,"given":"Dongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Kansas, Lawrence, KS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9363-738X","authenticated-orcid":false,"given":"Haifeng","family":"Chen","sequence":"additional","affiliation":[{"name":"NEC Laboratories America Inc, Princeton, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Panning for gold: Model-free knockoffs for high-dimensional controlled variable selection","author":"Cand\u00e8s Emmanuel Jean","year":"2016","unstructured":"Emmanuel Jean Cand\u00e8s, Yingying Fan, Lucas Janson, and Jinchi Lv. 2016. 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